With the research boom of deep learning, vehicle target detection has gradually changed from machine learning to deep learning in recent years. At present, most of the deep learning methods have different degrees of error detection and omission in vehicle target detection. The vehicle detection method based on incremental learning dataset is proposed to solve the problems of small targets error detection and the omission of truncated and overlapping targets. This method is combined with faster R-CNN algorithm to detect and classify vehicle targets. At the end of the experiment, the influence of with/without incremental learning method on the experimental results was compared from two aspects of subjective judgment and objective test data. The result show that the vehicle detection method based on incremental learning and faster R-CNN has significantly improved the performance in subjective judgment of the missed targets. Objective data also show that the VGG16 network mAP value is increased by 4% and the ResNet101 network mAP value is increased by 6% compared with the incremental learning method.